Field notes · page 2 of 3
AI Engineering Articles, Page 2
From the review desk
Production AI systems: inference and serving, retrieval, agents, evaluation and AI security, multimodal and edge pipelines, and AI-native product architecture.
Articles, page 2
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Durable Execution for Agents That Outlive a Web Request
Persisting checkpoints, replaying deterministically, handling human approval, and surviving crashes without duplicating side effects.
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Build an LLM Evaluation Harness That Can Block a Release
From task contracts and test slices to calibrated judges, regression budgets, and production feedback loops.
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Hallucination Controls Belong in the System, Not One Prompt
A layered design for constraining claims, grounding evidence, verifying outputs, and abstaining when the system does not know.
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Guardrails as Policy Enforcement, Not Keyword Blocking
Building layered input, action, and output controls that remain testable when language and threats change.
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Threat Modeling an AI Application End to End
Assets, trust boundaries, model-specific attacks, tool abuse, data leakage, and concrete mitigations for deployed AI systems.
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Prompt Injection Defense in Depth
Why delimiters are not a sandbox, how indirect injection reaches agents, and which architectural controls actually reduce impact.
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Embedding Systems: Models, Indexes, and Semantic Drift
Selecting representations, constructing embedding inputs, migrating indexes, and detecting when semantic neighborhoods stop serving the product.
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Fine-Tuning with LoRA in Production
When fine-tuning is justified, how low-rank adapters work, and what it takes to evaluate, serve, and update them safely.
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Multimodal AI Pipelines for Images, Audio, and Documents
Engineering preprocessing, temporal and spatial grounding, context budgets, validation, and storage around multimodal models.
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LLM Cost Engineering: From Token Prices to Unit Economics
Modeling full request cost, eliminating waste, forecasting margins, and optimizing without disguising quality regressions.
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Observability for LLM and Agent Systems
Tracing model calls, retrieval, tools, state transitions, quality signals, and cost without turning telemetry into a privacy liability.
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Model Routing, Cascades, and Fallbacks
How to choose models per request using task risk, calibrated confidence, operational health, and total expected cost.